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from ultralytics import YOLO
import streamlit as st
import cv2
import yt_dlp
import settings
def load_model(model_path):
"""
Loads a YOLO object detection model from the specified model_path.
Parameters:
model_path (str): The path to the YOLO model file.
Returns:
A YOLO object detection model.
"""
model = YOLO(model_path)
return model
def display_tracker_options():
display_tracker = st.radio("Display Tracker", ('Yes', 'No'))
is_display_tracker = True if display_tracker == 'Yes' else False
if is_display_tracker:
tracker_type = st.radio("Tracker", ("bytetrack.yaml", "botsort.yaml"))
return is_display_tracker, tracker_type
return is_display_tracker, None
def _display_detected_frames(conf, model, st_frame, image, is_display_tracking=None, tracker=None):
"""
Display the detected objects on a video frame using the YOLOv8 model.
Args:
- conf (float): Confidence threshold for object detection.
- model (YoloV8): A YOLOv8 object detection model.
- st_frame (Streamlit object): A Streamlit object to display the detected video.
- image (numpy array): A numpy array representing the video frame.
- is_display_tracking (bool): A flag indicating whether to display object tracking (default=None).
Returns:
None
"""
# Resize the image to a standard size
image = cv2.resize(image, (720, int(720*(9/16))))
# Display object tracking, if specified
if is_display_tracking:
res = model.track(image, conf=conf, persist=True, tracker=tracker)
else:
# Predict the objects in the image using the YOLOv8 model
res = model.predict(image, conf=conf)
# # Plot the detected objects on the video frame
res_plotted = res[0].plot()
st_frame.image(res_plotted,
caption='Detected Video',
channels="BGR",
use_column_width=True
)
def get_youtube_stream_url(youtube_url):
ydl_opts = {
'format': 'best[ext=mp4]',
'no_warnings': True,
'quiet': True
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
info = ydl.extract_info(youtube_url, download=False)
return info['url']
def play_youtube_video(conf, model):
source_youtube = st.sidebar.text_input("YouTube Video url")
is_display_tracker, tracker = display_tracker_options()
if st.sidebar.button('Detect Objects'):
if not source_youtube:
st.sidebar.error("Please enter a YouTube URL")
return
try:
st.sidebar.info("Extracting video stream URL...")
stream_url = get_youtube_stream_url(source_youtube)
st.sidebar.info("Opening video stream...")
vid_cap = cv2.VideoCapture(stream_url)
if not vid_cap.isOpened():
st.sidebar.error(
"Failed to open video stream. Please try a different video.")
return
st.sidebar.success("Video stream opened successfully!")
st_frame = st.empty()
while vid_cap.isOpened():
success, image = vid_cap.read()
if success:
_display_detected_frames(
conf,
model,
st_frame,
image,
is_display_tracker,
tracker
)
else:
break
vid_cap.release()
except Exception as e:
st.sidebar.error(f"An error occurred: {str(e)}")
def play_rtsp_stream(conf, model):
"""
Plays an rtsp stream. Detects Objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_rtsp = st.sidebar.text_input("rtsp stream url:")
st.sidebar.caption(
'Example URL: rtsp://admin:12345@192.168.1.210:554/Streaming/Channels/101')
is_display_tracker, tracker = display_tracker_options()
if st.sidebar.button('Detect Objects'):
try:
vid_cap = cv2.VideoCapture(source_rtsp)
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker,
tracker
)
else:
vid_cap.release()
break
except Exception as e:
vid_cap.release()
st.sidebar.error("Error loading RTSP stream: " + str(e))
def play_webcam(conf, model):
"""
Plays a webcam stream. Detects Objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_webcam = settings.WEBCAM_PATH
is_display_tracker, tracker = display_tracker_options()
if st.sidebar.button('Detect Objects'):
try:
vid_cap = cv2.VideoCapture(source_webcam)
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker,
tracker,
)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading video: " + str(e))
def play_stored_video(conf, model):
"""
Plays a stored video file. Tracks and detects objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_vid = st.sidebar.selectbox(
"Choose a video...", settings.VIDEOS_DICT.keys())
is_display_tracker, tracker = display_tracker_options()
with open(settings.VIDEOS_DICT.get(source_vid), 'rb') as video_file:
video_bytes = video_file.read()
if video_bytes:
st.video(video_bytes)
if st.sidebar.button('Detect Video Objects'):
try:
vid_cap = cv2.VideoCapture(
str(settings.VIDEOS_DICT.get(source_vid)))
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker,
tracker
)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading video: " + str(e))
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